iFQS: An Integrated FCNP-Q-Learning-Based Scheduling Algorithm for On-Demand Charging in Wireless Rechargeable Sensor Networks - Man Gun Ri; Chun Hyok Kim; Se Hun Pak; Chol Min Pong (International Journal of Distributed Sensor Networks)
In wireless rechargeable sensor networks (WRSNs), charging request nodes (RNs) are characterized by several criteria which are contradictory. Recently, on-demand charging scheduling schemes, which use two or more multicriteria decision-making (MCDM) methods, have been proposed. However, these on-demand charging schemes use a pairwise ratio scale which can magnify the actual pairwise difference between multicriteria and do not take into account the trade-off between performance metrics. In this paper, we propose a novel on-demand charging scheduling method using a fuzzy cognitive network process (FCNP) which uses a fuzzy pairwise interval scale to solve these issues. The proposed method, coined as an integrated FCNP-Q-learning-based scheduling (iFQS), first uses FCNP to exactly assign the relative weights to five multicriteria for charging prioritization and to three multicriteria for partial charging time (PCT) determination. Then, in charging path planning with Q-learning, the BS use these five criteria’s weights to design the reward function and select the most suitable next charging sojourn point. On the other hand, three criteria’s weights are also used to reasonably determine the PCT at charging sojourn points while achieving a desirable trade-off between charging metrics. The results of the extensive simulation show that the iFQS significantly improves charging performance in comparison with the existing MCDM-based methods. The network lifetime of the proposed method is 125.1%, 286.7%, and 368.5% longer than FAHP-VWA-TOPSIS, fuzzy Q-charging, and AHP-TOPSIS, respectively, when the number of nodes is 600.